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To improve the forecasting accuracies, researchers have long been using various combination techniques. In particular, the use of dissimilar methods for forecasting time series data is expected to provide superior results. Although numerous combination techniques have been proposed until date, the simple combination techniques —such as mean and median —maintain their strength, popularity, and utility. This paper proposes a new combination method based on the mean and median combination methods so as to combine the advantages of both these methods. The proposed combination technique attempts to utilize the strong aspects of each method and minimize the risk that arises from the selection of the combination method with poor performance. In order to depict the potential power of the proposed combining method, well-known six real-world time series data were used. Our results indicate that the proposed method presents with promising performances. In addition, a nonparametric statistical test was exploited to reveal the superiority of the proposed method over the single methods and other forecast combination methods from all of the investigated data sets.

There are a number of studies focusing on the relationship between the tobacco consumption and unemployment rate in Turkey. Therefore, this study contributes to the literature because the relationship between tobacco tax and unemployment rate has not been studied before. The objective of this study is to determine the relationship between the tobacco tax and unemployment rate in Turkey over the period 2006:1-2015:7. In this context, Unit root tests have been exploited to check the integration order of the variables, and the Autoregressive Distributed Lag Model including structural breaks is employed to explain the long-run relationship between the tobacco tax and unemployment rate. The findings of this study show that while an increase in unemployment rate is to raise tobacco tax in the long-run, an increase in unemployment rate is to decrease tobacco tax in the short-run.

This paper investigates the predictability of the asset prices of commodity transport (i.e. dry bulk carriers) by testing the shipping Q index as a leading indicator. We employ a comprehensive back-testing procedure with a broad spectrum of benchmark simulations. The shipping Q index (an adaptation of Tobin's Q index) has been introduced to benchmark models to observe predictive gain and interpret predictability features. This study presents a novel hybrid model to forecast time series data. The forecasting ability of the proposed hybrid algorithm is compared to specific univariate time series models, dynamic models, nonlinear models, and widely used hybrid models in the literature. The findings document that not only the proposed hybrid model performs better than the other competitive models in terms of hold out sample forecasting, but also using the shipping Q index improves the forecast accuracy by remarkably reducing forecasting error.

Electricity plays an important role in the South African economy with the industrial sector consuming the highest proportion followed by the residential and mining sector. Besides the fact that electricity is considered as an important energy sources, an adequate supply of electricity remains an important factor that affects the development and economic growth of a country. Therefore, it becomes even more important to forecast the total electricity production in South Africa. It turns out that the comparison of the predictive performance of different forecasting methods is inevitable. Hybrid forecasting approaches, such as artificial neural network (ANN) based seasonal Autoregressive Integrated Moving Average (sARIMA) model, ANN based multiplicative Holt-Winters (HW) model, ANN based additive HW model, an adaptive neuro-fuzzy inference system (ANFIS) based sARIMA model, ANFIS based multiplicative HW model and ANFIS based additive HW model, are employed as some valuable alternatives compared with the conventional univariate time series models, such as sARIMA model and both multiplicative and additive HW models. The aim of this study is not only to provide evidence on the weakness of the univariate time series models, but also to show that hybrid forecasting method has the superior ability over the univariate time series models, with achieving a higher forecasting accuracy. In addition, random walk model is used as benchmark model, allowing for the fair competition. The results show that the hybrid model, ANN based on multiplicative HW model, is the most fitted for the total electricity production in South Africa. This study presents an empirical framework to guide the field of prediction research by providing a more comprehensive empirical investigation of the total electricity production forecasting by using various hybrid models.